RapidMiner: Advanced Processes And Operators
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This document discusses advanced processes and operators in RapidMiner including feature selection, splitting processes, OLAP operators, post processing operators, and preprocessing operators. Feature selection uses the backward elimination algorithm to test which attributes are relevant for building a better model. Processes can be split into learning and applying sections. OLAP operators support tasks like grouping, aggregation, and pivoting for multidimensional analysis. Post processing operators perform actions after modeling like cost-sensitive threshold selection. Preprocessing operators generate new features or clean data by imputing missing values.
RapidMiner: Advanced Processes And Operators
- 1. RapidMiner52.4 Advanced processes and operators
- 2. Advanced ProcessesFeature Selection: Let us assume that we have a dataset with numerous attributes. We would like to test, whether all of these attributes are really relevant, or whether we can get a better model by omitting some of the original attributes. This task is called feature selection and the backward elimination algorithm is an approach that can solve it for you
- 3. Advanced ProcessesHere is how backward elimination works within RapidMiner:Enclose the cross-validation chain by a ‘FeatureSelection’ operator.
- 4. Advanced ProcessesSplitting a process:LearningProcessApplying
- 5. Advanced ProcessesSplitting a process
- 6. Advanced ProcessesSplitting a processLearningApplying
- 7. OLAP operators OLAP (Online Analytical Processing) is an approach to quickly providing answers to analytical queries that are multidimensional in nature. Usually, the basics of OLAP is a set of SQL queries which will typically result in a matrix (or pivot) format.
- 8. OLAP operators OLAP operators supported by RapidMinerGroupByAggregationGroupedANOVAAtrribute2ExamplePivotingExample2AttributePivoting
- 9. Post Processing operatorsPostprocessing operators can usually be applied on models in order to perform some postprocessing steps like cost-sensitive threshold selection or scalingschemeslike Platt scaling.
- 10. Post Processing operatorsPost-Processing operators in RapidMinerWindowExamples-2OriginalDataFrequentItem-SetUnificatorUncertain-Predictions-TransformationPlattScalingThresholdCreatorThresholdApplierThresholdFinder
- 11. Preprocessing OperatorsPreprocessing operators can be used to generate new features by applying functions on the existing features or by automatically cleaning up the data replacing missing values by, for instance, average values of this attribute.
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